Katie T. Freeman

1.8k total citations
43 papers, 1.4k citations indexed

About

Katie T. Freeman is a scholar working on Nutrition and Dietetics, Endocrine and Autonomic Systems and Molecular Biology. According to data from OpenAlex, Katie T. Freeman has authored 43 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Nutrition and Dietetics, 25 papers in Endocrine and Autonomic Systems and 16 papers in Molecular Biology. Recurrent topics in Katie T. Freeman's work include Biochemical Analysis and Sensing Techniques (28 papers), Regulation of Appetite and Obesity (25 papers) and Receptor Mechanisms and Signaling (15 papers). Katie T. Freeman is often cited by papers focused on Biochemical Analysis and Sensing Techniques (28 papers), Regulation of Appetite and Obesity (25 papers) and Receptor Mechanisms and Signaling (15 papers). Katie T. Freeman collaborates with scholars based in United States, Mexico and Italy. Katie T. Freeman's co-authors include Juan Miguel Jiménez‐Andrade, Joseph R. Ghilardi, Michael A. Kuskowski, Patrick W. Mantyh, Aaron P. Bloom, William G. Mantyh, Carrie Haskell‐Luevano, Magdalena Kaczmarska, Kathleen Coughlin and Gabriela Castañeda‐Corral and has published in prestigious journals such as Journal of Neuroscience, Oncogene and Pain.

In The Last Decade

Katie T. Freeman

40 papers receiving 1.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Katie T. Freeman United States	18	650	392	361	227	205	43	1.4k
Wen‐Li Mi China	29	814 1.3×	364 0.9×	268 0.7×	207 0.9×	49 0.2×	68	1.8k
Xin Luo United States	21	794 1.2×	464 1.2×	411 1.1×	182 0.8×	74 0.4×	41	1.7k
Nicole N. Scheff United States	19	379 0.6×	282 0.7×	265 0.7×	111 0.5×	50 0.2×	43	1.1k
Yue Sun China	25	667 1.0×	710 1.8×	253 0.7×	108 0.5×	51 0.2×	116	1.9k
Katharine Walker United States	15	768 1.2×	375 1.0×	360 1.0×	124 0.5×	52 0.3×	18	1.6k
Sarah Falk Denmark	16	451 0.7×	223 0.6×	168 0.5×	106 0.5×	23 0.1×	25	832
Gail J. Harty United States	19	723 1.1×	450 1.1×	562 1.6×	113 0.5×	22 0.1×	44	1.5k
Xu‐Hong Wei China	24	1.2k 1.8×	342 0.9×	657 1.8×	81 0.4×	26 0.1×	42	1.7k
Tim Hagenacker Germany	25	546 0.8×	658 1.7×	246 0.7×	188 0.8×	28 0.1×	110	1.8k
Kyle G. Halvorson United States	14	693 1.1×	226 0.6×	317 0.9×	233 1.0×	19 0.1×	21	1.3k

Countries citing papers authored by Katie T. Freeman

Since Specialization

Citations

This map shows the geographic impact of Katie T. Freeman's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Katie T. Freeman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katie T. Freeman more than expected).

Fields of papers citing papers by Katie T. Freeman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Katie T. Freeman. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Katie T. Freeman. The network helps show where Katie T. Freeman may publish in the future.

Co-authorship network of co-authors of Katie T. Freeman

This figure shows the co-authorship network connecting the top 25 collaborators of Katie T. Freeman. A scholar is included among the top collaborators of Katie T. Freeman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Katie T. Freeman. Katie T. Freeman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Bonet, J, Bridget Pierpont, Domenico Tricò, et al.. (2024). Rare variants in the melanocortin 4 receptor gene (MC4R) are associated with abdominal fat and insulin resistance in youth with obesity. International Journal of Obesity. 49(5). 819–826.

Ericson, Mark D., et al.. (2024). Incorporation of Three Extracyclic Arginine Residues into a Melanocortin Macrocyclic Agonist (c[Pro-His-DPhe-Arg-Trp-Dap-Lys(Arg-Arg-Arg-Ac)-DPro]) Decreases Food Intake When Administered Intrathecally or Subcutaneously Compared to a Macrocyclic Ligand Lacking Extracyclic Arginine Residues (c[Pro-His-DPhe-Arg-Trp-Dap-Ala-DPro)]. ACS Pharmacology & Translational Science. 7(4). 1114–1125.

Ericson, Mark D., Katie T. Freeman, Radleigh G. Santos, et al.. (2023). The Parallel Structure–Activity Relationship Screening of Three Compounds Identifies the Common Agonist Pharmacophore of Pyrrolidine Bis-Cyclic Guanidine Melanocortin-3 Receptor (MC3R) Small-Molecule Ligands. International Journal of Molecular Sciences. 24(12). 10145–10145. 2 indexed citations

Ericson, Mark D., et al.. (2023). Discovery of a Pan-Melanocortin Receptor Antagonist [Ac-DPhe(pI)-Arg-Nal(2′)-Orn-NH₂] at the MC1R, MC3R, MC4R, and MC5R that Mediates an Increased Feeding Response in Mice and a 40-Fold Selective MC1R Antagonist [Ac-DPhe(pI)-DArg-Nal(2′)-Arg-NH₂]. Journal of Medicinal Chemistry. 66(12). 8103–8117. 1 indexed citations

Koikov, L. N., Renny J. Starner, Viki B. Swope, et al.. (2021). Development of hMC1R Selective Small Agonists for Sunless Tanning and Prevention of Genotoxicity of UV in Melanocytes. Journal of Investigative Dermatology. 141(7). 1819–1829. 17 indexed citations

Ericson, Mark D., Katie T. Freeman, Zhimin Xiang, et al.. (2021). Discovery of Molecular Interactions of the Human Melanocortin-4 Receptor (hMC4R) Asp189 (D189) Amino Acid with the Endogenous G-Protein-Coupled Receptor (GPCR) Antagonist Agouti-Related Protein (AGRP) Provides Insights to AGRP’s Inverse Agonist Pharmacology at the hMC4R. ACS Chemical Neuroscience. 12(3). 542–556. 4 indexed citations

Ericson, Mark D., Katie T. Freeman, & Carrie Haskell‐Luevano. (2020). Peptoid NPhe⁴ in AGRP-Based c[Pro¹-Arg²-Phe³-Phe⁴-Xxx⁵-Ala⁶-Phe⁷-DPro⁸] Scaffolds Maintain Mouse MC4R Antagonist Potency. ACS Medicinal Chemistry Letters. 11(10). 1942–1948. 1 indexed citations

Ericson, Mark D., Katie T. Freeman, Robert C. Speth, et al.. (2019). Incorporation of Agouti-Related Protein (AgRP) Human Single Nucleotide Polymorphisms (SNPs) in the AgRP-Derived Macrocyclic Scaffold c[Pro-Arg-Phe-Phe-Asn-Ala-Phe-dPro] Decreases Melanocortin-4 Receptor Antagonist Potency and Results in the Discovery of Melanocortin-5 Receptor Antagonists. Journal of Medicinal Chemistry. 63(5). 2194–2208. 6 indexed citations

Ericson, Mark D., et al.. (2018). Arg-Phe-Phe d-Amino Acid Stereochemistry Scan in the Macrocyclic Agouti-Related Protein Antagonist Scaffold c[Pro-Arg-Phe-Phe-Xxx-Ala-Phe-DPro] Results in Unanticipated Melanocortin-1 Receptor Agonist Profiles. ACS Chemical Neuroscience. 9(12). 3015–3023. 4 indexed citations

10.

Freeman, Katie T., Marvin L. Dirain, Radleigh G. Santos, et al.. (2017). Discovery of Mixed Pharmacology Melanocortin-3 Agonists and Melanocortin-4 Receptor Tetrapeptide Antagonist Compounds (TACOs) Based on the Sequence Ac-Xaa¹-Arg-(pI)DPhe-Xaa⁴-NH₂. Journal of Medicinal Chemistry. 60(10). 4342–4357. 14 indexed citations

11.

Adank, Danielle N., et al.. (2017). A Direct in Vivo Comparison of the Melanocortin Monovalent Agonist Ac-His-DPhe-Arg-Trp-NH₂ versus the Bivalent Agonist Ac-His-DPhe-Arg-Trp-PEDG20-His-DPhe-Arg-Trp-NH₂: A Bivalent Advantage. ACS Chemical Neuroscience. 8(6). 1262–1278. 16 indexed citations

12.

Freeman, Katie T., et al.. (2016). An in Vitro and in Vivo Investigation of Bivalent Ligands That Display Preferential Binding and Functional Activity for Different Melanocortin Receptor Homodimers. Journal of Medicinal Chemistry. 59(7). 3112–3128. 40 indexed citations

13.

Singh, Anamika, et al.. (2015). Synthesis and Pharmacology of α/β³-Peptides Based on the Melanocortin Agonist Ac-His-dPhe-Arg-Trp-NH₂ Sequence. ACS Medicinal Chemistry Letters. 6(5). 568–572. 25 indexed citations

14.

Ghilardi, Joseph R., Katie T. Freeman, Juan Miguel Jiménez‐Andrade, et al.. (2012). Neuroplasticity of sensory and sympathetic nerve fibers in a mouse model of a painful arthritic joint. Arthritis & Rheumatism. 64(7). 2223–2232. 128 indexed citations

15.

Bloom, Aaron P., Juan Miguel Jiménez‐Andrade, Gabriela Castañeda‐Corral, et al.. (2011). Breast Cancer-Induced Bone Remodeling, Skeletal Pain, and Sprouting of Sensory Nerve Fibers. Journal of Pain. 12(6). 698–711. 141 indexed citations

16.

Jiménez‐Andrade, Juan Miguel, Aaron P. Bloom, William G. Mantyh, et al.. (2010). Pathological Sprouting of Adult Nociceptors in Chronic Prostate Cancer-Induced Bone Pain. Journal of Neuroscience. 30(44). 14649–14656. 153 indexed citations

17.

Mantyh, William G., Juan Miguel Jiménez‐Andrade, Aaron P. Bloom, et al.. (2010). Blockade of nerve sprouting and neuroma formation markedly attenuates the development of late stage cancer pain. Neuroscience. 171(2). 588–598. 141 indexed citations

18.

Ghilardi, Joseph R., Katie T. Freeman, Juan Miguel Jiménez‐Andrade, et al.. (2010). Sustained blockade of neurotrophin receptors TrkA, TrkB and TrkC reduces non-malignant skeletal pain but not the maintenance of sensory and sympathetic nerve fibers. Bone. 48(2). 389–398. 53 indexed citations

19.

Freeman, Katie T., Nathan J. Koewler, Juan Miguel Jiménez‐Andrade, et al.. (2008). A Fracture Pain Model in the Rat. Anesthesiology. 108(3). 473–483. 46 indexed citations

20.

Jiménez‐Andrade, Juan Miguel, Nathan J. Koewler, Katie T. Freeman, et al.. (2007). Nerve growth factor sequestering therapy attenuates non-malignant skeletal pain following fracture. Pain. 133(1). 183–196. 94 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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